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Samskara minimal structural features for detecting subjectivity and polarity in Italian tweets

Conference Paper
Publication Date:
2016
abstract:
Sentiment analysis classification tasks strongly depend on the properties of the medium that is used to communicate opinionated content. There are some limitations in Twitter that force the user to exploit structural properties of this social network with features that have pragmatic and communicative functions. Samskara is a system that uses minimal structural features to classify Italian tweets as instantiations of a textual genre, obtaining good results for subjectivity classification, while polarity classification needs substantial improvements.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
sentiment analysis; twitter
List of contributors:
Russo, Irene; Monachini, Monica
Authors of the University:
MONACHINI MONICA
RUSSO IRENE
Handle:
https://iris.cnr.it/handle/20.500.14243/355296
Published in:
CEUR WORKSHOP PROCEEDINGS
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http://www.scopus.com/record/display.url?eid=2-s2.0-85009270160&origin=inward
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